OBJECTIVE: To analyze the current situation and developing trend of drugs used in 10 hospitals of Hunan Province from 1996 to 2000, and to serve as reference for drug production, marketing and clinical application .METHODS:The sum of money and percentages of different kinds of drugs used each year were added up separately.RESULTS: The sum of money of drugs consumed was increasing at a rate of 18% each year.About 40% of the money was spent on anti - infective drugs and 20% on imported drugs.CONCLUSION: The sum of money of drugs consumed will grow year by year .The clinical use of drugs will be more rational and of more choices,but the sum of money spent on imported drugs will decrease.

Objective To observe the effect of curcumin on behavior,blood brain barrier(BBB)and ex-pression of glial fibrillary acidic protein(GFAP)and cyclic nucleotide 3′phosphohydrolase(CNPase)in hippocam-pus of radiation injured brain(RIB)rats. Methods SD rats were divided into radiation group,treatment group and negative control group. RIB rats model were established by X ray,and rats in treatment group were treated by curcumin. Morris water maze test were taken to study learning memory of rats in each group. The expression of Ev-ans blue(EB)in brain tissue and the expression of GFAP and CNPase in hippocampus were detected to observe the effect of curcumin on the BBB of RIB rats. Results In RIB rats,learning memory were decreased significant-ly,permeability of BBB were increased. GFAP expression in brain tissue was increased,and CNPase was de-creased(P < 0.05). After the treatment of curcumin,learning memory of rats were improved,the permeability of BBB was decreased,GFAP was decreased,and CNPase expression was increased(P < 0.05). Conclusion Cur-cumin can significantly reduce the damage of BBB in RIB rats,decrease the expression of GFAP and increase the expression of CNPase in hippocampal,which indicate that curcumin has curative effect on radiation injured brain.

Objective: To construct a precise model for identifying traditional Chinese medicine (TCM) constitutions, thereby offering optimized guidance for clinical diagnosis and treatment planning, and ultimately enhancing medical efficiency and treatment outcomes. Methods: First, TCM full-body inspection data acquisition equipment was employed to collect full-body standing images of healthy people, from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire (CCMQ), and a dataset encompassing labelled constitutions was constructed. Second, heat-suppression valve (HSV) color space and improved local binary patterns (LBP) algorithm were leveraged for the extraction of features such as facial complexion and body shape. In addition, a dual-branch deep network was employed to collect deep features from the full-body standing images. Last, the random forest (RF) algorithm was utilized to learn the extracted multifeatures, which were subsequently employed to establish a TCM constitution identification model. Accuracy, precision, and F1 score were the three measures selected to assess the performance of the model. Result: It was found that the accuracy, precision, and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842, 0.868, and 0.790, respectively. In comparison with the identification models that encompass a single feature, either a single facial complexion feature, a body shape feature, or deep features, the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105, 0.105, and 0.079, the precision increased by 0.164, 0.164, and 0.211, and the F1 score rose by 0.071, 0.071, and 0.084, respectively. Conclusion The research findings affirmed the viability of the proposed model, which incorporated multifeatures, including the facial complexion feature, the body shape feature, and the deep feature. In addition, by employing the proposed model, the objectification and intelligence of identifying constitutions in TCM practices could be optimized.